Integrative Multi-Omics Reveals Distinct Molecular Signatures of Metabolic Syndrome from KORA FF4 Study

Background/Objectives: Metabolic syndrome (MetS) is a heterogeneous cluster of cardiometabolic risk factors whose underlying molecular heterogeneity remains incompletely understood. Methods: Targeted metabolomics (388 metabolites) and untargeted proteomics (842 proteins) were analyzed in the KORA FF4 study (n = 1921). Multivariable regression, LASSO, and Multi-Omics Factor Analysis (MOFA) were applied to identify MetS-associated signatures. As a secondary analysis, soft-clustering archetypal analysis based on 22 clinical variables was performed within the MetS subgroup (n = 582) to identify extreme clinical subphenotypes. Results: A total of 117 metabolites and 70 proteins were associated with MetS, including five metabolites and eight proteins shared across all MetS traits. Key metabolomic features comprised diacylglycerols, triglyceride score, hexoses, and branched-chain amino acids. Prominent proteins included APOC4, APOE, APOB, APOD, APOF, adiponectin, LBP, and SHBG, reflecting alterations in lipid metabolism, inflammation, and metabolic regulation. MOFA identified a latent factor strongly linked to MetS, integrating signatures of lipid metabolism, insulin resistance, and inflammation. Archetypal analysis identified extreme MetS profiles and associated with metabolites and proteins shared across all Mets. Conclusions: Integrated multi-omics analysis revealed MetS-associated molecular signatures characterized primarily by dysregulated lipid metabolism, insulin resistance, and chronic low-grade inflammation. Secondary archetypal analysis highlighted clinical heterogeneity at the extreme boundaries of MetS while supporting a shared molecular foundation. These findings highlight the value of combining clinical, metabolomics and proteomics to improve characterization of MetS heterogeneity and inform future precision risk stratification.